Create an Object of Class spsurvey.analysis

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Description

This function creates an object of class spsurvey.analysis that contains all of the information necessary to use the analysis functions in the spsurvey library.

Usage

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spsurvey.analysis(sites=NULL, subpop=NULL, design=NULL, data.cat=NULL,
  data.cont=NULL, siteID=NULL, wgt=NULL, sigma=NULL, var.sigma=NULL,
  xcoord=NULL, ycoord=NULL, stratum=NULL, cluster=NULL, wgt1=NULL, xcoord1=NULL,
  ycoord1=NULL, popsize=NULL, popcorrect=FALSE, pcfsize=NULL, N.cluster=NULL,
  stage1size=NULL, support=NULL, sizeweight=FALSE, swgt=NULL, swgt1=NULL,
  vartype="Local", conf=95, pctval=c(5,10,25,50,75,90,95))

Arguments

sites

a data frame consisting of two variables: the first variable is site IDs and the second variable is a logical vector indicating which sites to use in the analysis. If this data frame is not provided, then the data frame will be created, where (1) site IDs are obtained either from the design argument, the siteID argument, or both (when siteID is a formula); and (2) a variable named use.sites that contains the value TRUE for all sites is created. The default is NULL.

subpop

a data frame describing sets of populations and subpopulations for which estimates will be calculated. The first variable is site IDs and each subsequent variable identifies a Type of population, where the variable name is used to identify Type. A Type variable identifies each site with one of the subpopulations of that Type. If this data frame is not provided, then the data frame will be created, where (1) site IDs are obtained either from the design argument, the siteID argument, or both (when siteID is a formula); and (2) a single Type variable named all.sites that contains the value "All Sites" for all sites is created. The default is NULL.

design

a data frame consisting of design variables. If variable names are provided as formulas in the corresponding arguments, then the formulas are interpreted using this data frame. If this data frame is not provided, then the data frame will be created from inputs to the design variables in the argument list. The default is NULL. If variable names are not provided as formulas, then variables should be named as follows:
siteID = site IDs
wgt = final adjusted weights
xcoord = x-coordinates for location
ycoord = y-coordinates for location
stratum = stratum codes
cluster = stage one sampling unit codes
wgt1 = final adjusted stage one weights
xcoord1 = stage one x-coordinates for location
ycoord1 = stage one y-coordinates for location
support = support values
swgt = size-weights
swgt1 = stage one size-weights

data.cat

a data frame of categorical response variables. The first variable is site IDs. Subsequent variables are response variables. Missing data (NA) is allowed. The default is NULL.

data.cont

a data frame of continuous response variables. The first variable is site IDs. Subsequent variables are response variables. Missing data (NA) is allowed. The default is NULL.

siteID

site IDs. This variable can be input directly or as a formula and must be supplied either as this argument or in the design data frame. The default is NULL.

wgt

the final adjusted weight (inverse of the sample inclusion probability) for each site, which is either the weight for a single-stage sample or the stage two weight for a two-stage sample. The default is NULL.

sigma

measurement error variance. This variable must be a vector containing a value for each response variable and must have the names attribute set to identify the response variable names. Missing data (NA) is allowed. The default is NULL.

var.sigma

variance of the measurement error variance. This variable must be a vector containing a value for each response variable and must have the names attribute set to identify the response variable names. Missing data (NA) is allowed. The default is NULL.

xcoord

x-coordinate for location for each site, which is either the x-coordinate for a single-stage sample or the stage two x-coordinate for a two-stage sample. The default is NULL.

ycoord

y-coordinate for location for each site, which is either the y-coordinate for a single-stage sample or the stage two y-coordinate for a two-stage sample. The default is NULL.

stratum

the stratum codes. This variable can be input directly or as a formula. The default is NULL.

cluster

the stage one sampling unit (primary sampling unit or cluster) codes. This variable can be input directly or as a formula. The default is NULL.

wgt1

the final adjusted stage one weights. This variable can be input directly or as a formula. The default is NULL.

xcoord1

the stage one x-coordinates for location. This variable can be input directly or as a formula. The default is NULL.

ycoord1

the stage one y-coordinates for location. This variable can be input directly or as a formula. The default is NULL.

popsize

known size of the resource, which is used to perform ratio adjustment to estimators expressed using measurement units for the resource. For a finite resource, this argument is either the total number of sampling units or the known sum of size-weights. For an extensive resource, this argument is the measure of the resource, i.e., either known total length for a linear resource or known total area for an areal resource. The argument must be in the form of a list containing an element for each population Type in the subpop data frame, where NULL is a valid choice for a population Type. The list must be named using the column names for the population Types in subpop. If a population Type doesn't contain subpopulations, then each element of the list is either a single value for an unstratified sample or a vector containing a value for each stratum for a stratified sample, where elements of the vector are named using the stratum codes. If a population Type contains subpopulations, then each element of the list is a list containing an element for each subpopulation, where the list is named using the subpopulation names. The element for each subpopulation will be either a single value for an unstratified sample or a named vector of values for a stratified sample. The default is NULL.

Example popsize for a stratified sample:
popsize = list("Pop 1"=c("Stratum 1"=750,
"Stratum 2"=500,
"Stratum 3"=250),
"Pop 2"=list("SubPop 1"=c("Stratum 1"=350,
"Stratum 2"=250,
"Stratum 3"=150),
"SubPop 2"=c("Stratum 1"=250,
"Stratum 2"=150,
"Stratum 3"=100),
"SubPop 3"=c("Stratum 1"=150,
"Stratum 2"=150,
"Stratum 3"=75)),
"Pop 3"=NULL)

Example popsize for an unstratified sample:
popsize = list("Pop 1"=1500,
"Pop 2"=list("SubPop 1"=750,
"SubPop 2"=500,
"SubPop 3"=375),
"Pop 3"=NULL)

popcorrect

a logical value that indicates whether finite or continuous population correction factors should be employed during variance estimation, where TRUE = use the correction factor and FALSE = do not use the correction factor. The default is FALSE. To employ the correction factor for a single-stage sample, values must be supplied for argument pcfsize and for the support variable of the design argument. To employ the correction factor for a two-stage sample, values must be supplied for arguments N.cluster and stage1size, and for the support variable of the design argument.

pcfsize

size of the resource, which is required for calculation of finite and continuous population correction factors for a single-stage sample. For a stratified sample this argument must be a vector containing a value for each stratum and must have the names attribute set to identify the stratum codes. The default is NULL.

N.cluster

the number of stage one sampling units in the resource, which is required for calculation of finite and continuous population correction factors for a two-stage sample. For a stratified sample this variable must be a vector containing a value for each stratum and must have the names attribute set to identify the stratum codes. The default is NULL.

stage1size

size of the stage one sampling units of a two-stage sample, which is required for calculation of finite and continuous population correction factors for a two-stage sample and must have the names attribute set to identify the stage one sampling unit codes. For a stratified sample, the names attribute must be set to identify both stratum codes and stage one sampling unit codes using a convention where the two codes are separated by the & symbol, e.g., "Stratum 1&Cluster 1". The default is NULL.

support

the support value for each site - the value one (1) for a site from a finite resource or the measure of the sampling unit associated with a site from an extensive resource, which is required for calculation of finite and continuous population correction factors. This variable can be input directly or as a formula. The default is NULL.

sizeweight

a logical value that indicates whether size-weights should be used in the analysis, where TRUE = use the size-weights and FALSE = do not use the size-weights. The default is FALSE.

swgt

the size-weight for each site, which is the stage two size-weight for a two-stage sample. This variable can be input directly or as a formula. The default is NULL.

swgt1

the stage one size-weight for each site. This variable can be input directly or as a formula. The default is NULL.

vartype

the choice of variance estimator, where "Local" = local mean estimator and "SRS" = SRS estimator. The default is "Local".

conf

the confidence level. The default is 95%.

pctval

the set of values at which percentiles are estimated. The default set is: {5, 10, 25, 50, 75, 90, 95}.

Value

Value is a list of class spsurvey.analysis. Only those sites indicated by the logical variable in the sites data frame are retained in the output. The sites, subpop, and design data frames will always exist in the output. At least one of the data.cat and data.cont data frames will exist. Depending upon values of the input variables, other elements in the output may be NULL. The list is composed of the following components:

  • sites - the sites data frame

  • subpop - the subpop data frame

  • design - the design data frame

  • data.cat - the data.cat data frame

  • data.cont - the data.cont data frame

  • sigma - measurement error variance

  • var.sigma - variance of the estimated measurement error variance

  • stratum.ind - a logical value that indicates whether the sample is stratified, where TRUE = a stratified sample and FALSE = not a stratified sample

  • cluster.ind - a logical value that indicates whether the sample is a two-stage sample, where TRUE = a two-stage sample and FALSE = not a two-stage sample

  • popsize - the known size of the resource

  • pcfactor.ind - a logical value that indicates whether the population correction factor is used during variance estimation, where TRUE = use the population correction factor and FALSE = do not use the factor

  • pcfsize - size of the resource, which is required for calculation of finite and continuous population correction factors for a single-stage sample

  • N.cluster - the number of stage one sampling units in the resource

  • stage1size - the known size of the stage one sampling units

  • swgt.ind - a logical value that indicates whether the sample is a size-weighted sample, where TRUE = a size-weighted sample and FALSE = not a size-weighted sample

  • vartype - the choice of variance estimator, where "Local" = local mean estimator and "SRS" = SRS estimator

  • conf - the confidence level

  • pctval - the set of values at which percentiles are estimated, where the default set is: 5, 25, 50, 75, 95

Author(s)

Tom Kincaid Kincaid.Tom@epa.gov

References

Diaz-Ramos, S., D.L. Stevens, Jr., and A.R. Olsen. (1996). EMAP Statistical Methods Manual. EPA/620/R-96/XXX. Corvallis, OR: U.S. Environmental Protection Agency, Office of Research and Development, National Health Effects and Environmental Research Laboratory, Western Ecology Division.

See Also

cat.analysis, cont.analysis

Examples

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# Categorical variable example:
mysiteID <- paste("Site", 1:100, sep="")
mysites <- data.frame(siteID=mysiteID, Active=rep(TRUE, 100))
mysubpop <- data.frame(siteID=mysiteID, All.Sites=rep("All Sites", 100),
   Resource.Class=rep(c("Good","Poor"), c(55,45)))
mydesign <- data.frame(siteID=mysiteID, wgt=runif(100, 10,
   100), xcoord=runif(100), ycoord=runif(100), stratum= rep(c("Stratum1",
   "Stratum2"), 50))
mydata.cat <- data.frame(siteID=mysiteID, CatVar= rep(c("north", "south",
   "east", "west"), 25))
mypopsize <- list(All.Sites=c(Stratum1=3500, Stratum2=2000),
   Resource.Class=list(Good=c(Stratum1=2500, Stratum2=1500),
   Poor=c(Stratum1=1000, Stratum2=500)))
spsurvey.analysis(sites=mysites, subpop=mysubpop, design=mydesign,
   data.cat=mydata.cat, popsize=mypopsize)

# Continuous variable example - including deconvolution estimates:
mydesign <- data.frame(ID=mysiteID, wgt=runif(100, 10, 100),
   xcoord=runif(100), ycoord=runif(100), stratum=rep(c("Stratum1",
   "Stratum2"), 50))
ContVar <- rnorm(100, 10, 1)
mydata.cont <- data.frame(siteID=mysiteID, ContVar=ContVar,
   ContVar.1=ContVar + rnorm(100, 0, sqrt(0.25)),
   ContVar.2=ContVar + rnorm(100, 0, sqrt(0.50)))
mysigma <- c(ContVar=NA, ContVar.1=0.25, ContVar.2=0.50)
spsurvey.analysis(sites=mysites, subpop=mysubpop, design=mydesign,
   data.cont=mydata.cont, siteID=~ID, sigma=mysigma,
   popsize=mypopsize)

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